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Backend Engineer, Forward Deployed

Job in San Diego, San Diego County, California, 92101, USA
Listing for: Foxglove
Full Time position
Listed on 2026-11-01
Job specializations:
  • Software Development
    Robotics
Salary/Wage Range or Industry Benchmark: 140000 - 190000 USD Yearly USD 140000.00 190000.00 YEAR
Job Description & How to Apply Below
Build the data infrastructure that powers physical AI.

Physical AI is moving from research labs into production fleets across industries. As robots scale across the real world, from factories to vehicles, to defense - every workflow from product development to deployment becomes a data problem: what happened, when, on which robot, and why?

At Foxglove, we built the unified data platform for physical AI that developer and engineering teams use to answer those questions. We help teams make vast quantities of robotics data actionable, creating the data flywheel they need to develop, test, train, deploy, and operate robots with confidence.

About the Role

We're looking for an engineer that is fluent in Korean and English, who wants to work directly with customers. You're product-minded, good with customers and keen on robotics. You'll build and deploy Foxglove solutions for robotics teams in Korea, including at sites with no internet access.

You'll work with robotics companies in Korea and own projects end to end to speed up their development on the Foxglove platform. You'll learn what holds them back, work with our design and product teams on solutions, build those solutions and help customers adopt them. What you learn goes back to our product team and into the roadmap.

We're looking for strong full-stack or backend engineers.

Key Responsibilities
  • Work closely with customer teams, in Korean and English, to understand their goals, constraints and technical needs.
  • Design and build end-to-end solutions in small, tested steps (Type Script/React, Node.js, Python, ROS, SQL and/or Rust).
  • Write clear scopes, timelines and success measures, from proof of concept to production.
  • Code alongside customer engineers and deploy in their cloud, on-prem and offline environments.
  • Install and update software at customer sites with no internet access, and write the runbooks for it.
  • Train customer teams and create docs in Korean and English.
  • Work with Sales, Solutions Engineering and Customer Success on the same accounts.
  • Bring what you learn from customers back to product, design and engineering.
  • Document what works, contribute to internal libraries and help the FDE team support Korean customers.
What We’re Looking For
  • Bachelor’s degree in Computer Science, Engineering or a related field, or equivalent experience.
  • Fluent Korean and fluent English, spoken and written.
  • You've run technical meetings with customer engineers and turned their requests into scoped work.
  • A track record of fast prototyping and step-by-step delivery with external stakeholders.
  • Backend experience in Node.js, Python or Rust, and frontend experience in React/Type Script. C++ is a plus.
  • Docker, and comfort deploying and supporting software on networks with no internet access.
  • You can judge what to build and make it usable without a designer.
  • Able to work on your own with a team 16 to 17 hours behind you.
  • Comfortable working on site with customers in Korea when needed.
  • Comfortable with up to 50% travel, including to Korea.
  • Bonus: robotics or industrial automation experience, or a past deployed role.
Why join Foxglove
  • Work on real robotics problems. Robot data is large, messy, multimodal, time-sensitive, and tied to physical-world behavior. The problems we work on span ingestion, indexing, search, visualization, replay, connectivity, collaboration, evaluation, and operations.
  • Build tools engineers rely on. Foxglove is used by robotics teams investigating failures, validating changes, reviewing field behavior, curating datasets, and operating production fleets. The work you do helps teams understand what their robots saw, what they did, and why they behaved the way they did.
  • High-leverage product surface area. A better query path, visualization workflow, Fleet…
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